Toward A Framework For Data Quality In Cloud- Based Health Information System
|
|
- Lambert Sharp
- 8 years ago
- Views:
Transcription
1 Toward A Framework For Data Quality In Cloud- Based Health Information System Omar Almutiry, Gary Wills, Abdulelah Alwabel, Richard Crowder and Robert Walters Electronics and Computer Science University of Southampton Southampton, UK {osa1a11,gbw,aa1a10,rmc,rjw1}@ecs.soton.ac.uk Abstract This Cloud computing is a promising platform for health information systems in order to reduce costs and improve accessibility. Cloud computing represents a shift away from computing being purchased as a product to be a service delivered over the Internet to customers. Cloud computing paradigm is becoming one of the popular IT infrastructures for facilitating Electronic Health Record (EHR) integration and sharing. EHR is defined as a repository of patient data in digital form. This record is stored and exchanged securely and accessible by different levels of authorized users. Its key purpose is to support the continuity of care, and allow the exchange and integration of medical information for a patient. However, this would not be achieved without ensuring the quality of data populated in the healthcare clouds as the data quality can have a great impact on the overall effectiveness of any system. The assurance of the quality of data used in healthcare systems is a pressing need to help the continuity and quality of care. Identification of data quality dimensions in healthcare clouds is a challenging issue as data quality of cloud-based health information systems arise some issues such as the appropriateness of use, and provenance. Some research proposed frameworks of the data quality dimensions without taking into consideration the nature of cloudbased healthcare systems. In this paper, we proposed an initial framework that fits the data quality attributes. This framework reflects the main elements of the cloud-based healthcare systems and the functionality of EHR. Health Information System(HIS), Electronic Health Record (EHR), Data Quality (DQ), DQ Dimensions, Cloud Computing I. INTRODUCTION Electronic Health Record (EHR) refers to the digital form of a patient s medical record. It is defined as a repository of patient data in digital form. This record can be stored and exchanged securely and is accessible by different levels of authorized users (Häyrinen, Saranto, & Nykänen, 2008). Enhancing the quality of care is a noticeable advantage of adopting EHR systems. Many studies (Thakkar & Davis, 2006; Yoon-Flannery, 2008) have highlighted how such systems could enhance quality of care and support its continuity. Cloud computing is a promising platform for health information systems in order to reduce costs and improve accessibility. Cloud computing represents a shift away from computing being purchased as a product to be a service delivered over the Internet to customers. Economic benefits are the key role behind the appearance of cloud computing (Buyya, Yeo, Venugopal, Broberg, & Brandic, 2009). The Cloud transforms IT assets from being capital expenditure to be operational expenditure. Traditionally, small and medium enterprises obtain IT infrastructure by purchasing it. In the cloud, using a server for five hours costs the same as using five servers for an hour (Armbrust et al., 2010). Data quality in information systems and its dimensions have been widely discussed by many researchers (Ballou & Pazer 1985; Tayi & Ballou 1998; Strong et al. 1997; Wang et al. 1995; Fox et al. 1994; Levitin & Redman 1995; Canadian Institute for Health Information 2009; Orfanidis et al. 2004). As a result, many frameworks of dimensions to assure data quality have been introduced and discussed. However, these frameworks have missed some important dimensions needed to ensure, for example, the integrity and origin of information (provenance). These missing dimensions are because the frameworks are generic and do not reflect the nature of the domain. In the area of Health Information System, Data quality assurance is a challenging issue as the key barriers of optimally using data populated in cloud-based EHRs is the increasing data quantity with poor quality. Fitness for use is one of the best definitions of the data quality. This definition takes us even further beyond the traditional concerns with accuracy of data, as it will end up with many dimensions of data quality. So data quality is a concept with multi-dimensions. Therefore, we developed an initial framework that concerns DQ in the context of cloud-based HIS. This framework is a result of filtering the existing data quality dimensions in many research, and checking their suitability to the nature of healthcare clouds. This paper reviewed the notion of cloud computing, cloudbased EHR systems and their functionalities, and data quality. After that, it discussed the proposed framework and its life development. The paper concludes with discussion and future work. II. HEALTHCARE CLOUD In this section we briefly discus the notion of cloud computing and its potential in HIS. Then we briefly define the concept of personal Health Record (PHR), Electronic Medical Record (EMR) and Electronic Health Record (EHR). After
2 that, we study the functionalities of these systems which would help us identify the data quality dimensions used to measure and assess the quality of such systems. A. Cloud computing and its attraction to healthcare IT Cloud computing is a promising platform for EHR in order to reduce costs and improve accessibility. Cloud computing represents a shift away from computing being purchased as a product to be a service delivered over the Internet to customers. Economic benefits are the key role behind the appearance of cloud computing (Buyya et al., 2009). The Cloud transforms IT assets from being capital expenditure to be operational expenditure. Traditionally, small and medium enterprises obtain IT infrastructure by purchasing it. In the cloud, using a server for five hours costs the same as using five servers for an hour (Armbrust et al., 2010). There are three common services delivered by Cloud: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). The underline infrastructure of a Cloud is consisted of one or more data centres, each has a massive number of computing resources. The IaaS delivery model allows users to acquire and release infrastructure resources (e.g. CPU and storage). PaaS offers a platform for developing end-to-end life cycle software development (Rimal, Choi, & Lumb, 2009) which contains development environment, set of applications to allow writing code, a set of ready packages to be used by other software and libraries (Hammond, Hawtin, Gillam, & Oppenheim, 2010). SaaS is a delivery model of applications provided by the Cloud to be run by Cloud users through web tools such as web services. This is the most abstract model of services, where users have no control over the Cloud infrastructure (Dillon, Wu, & Chang, 2010). B. Cloud-based HIS-related challenges and issues Some researches (Kuo, 2011; Zhang & Liu, 2010) highlighted some challenges and issues that could affect the adoption of this technology in healthcare field. The main concern is the lack trust in data security and privacy by users, the loss of governance and uncertain provider s compliance. This is due to the nature of this technology as it allows accessibility to different users. These issues will certainly affect the quality of data resided on cloud-based systems. The notion of cloud computing supports the accessibility from different sites and level of people. So there is a pressing need for assuring the quality of such system as it is a valuable source for the health stakeholders for their decisions. C. The definitions of different types of healthcare systems There are many terms that defined the patient-related electronic information in e-health services. These terms, EHR, EMR and EPR, are often used interchangeably in the healthcare filed despite the vital deference between these terminologies. Some people have confused EMR and EHR in spite of the fact that they describe the completely different concepts (Garets & Davis, 2006). EMRs (Wager et al. 2009; Garets & Davis 2006) is a type of application environment composed of electronic records of health-related information, such as clinical data, order entries and pharmacy information. Health stakeholders use these databases to document, monitor and manage care delivery within a Care Delivery Organisation (CDO). The data in an EMR is a legal record owned by the CDO and audits what happened to patients during their encounters in the health care organisation. EMRs are widely used in North America and Japan but are regarded as outdated by many (Kim & Lehmann 2009). Personal Health Record (PHR) is defined by some researchers (Alliance & Coordinator 2008; Wager et al. 2009) as an electronic record of an individual s health-related information drawn from heterogeneous sources and managed and controlled by the individual. Such a record must comply with nationally recognized interoperability standards. EHR refers to the digital form of a patient s medical record. It is defined as a repository of patient data in digital form. This record can be stored and exchanged securely and is accessible by different levels of authorized users (Häyrinen et al., 2008). What distinguishes EHR from EMR is that EHR combines electronic information of a patient from different care settings held in various healthcare systems. D. The functionalities of healthcare systems The Institute of Medicine (IOM) Committee in the USA (Hoffman & Podgurski, 2008) identified the key components of EHR systems and highlighted its functionalities. These core functionalities fall into eight categories, and are briefly discussed below: Health Information and Data: EHR systems should hold a defined data set that includes, for example, medical and nursing diagnoses, allergies, demographics and laboratory test results to ensure improved access by care stakeholders to needed information. Results Management: This feature manages results of all types, such as laboratory test results and radiology procedure results reports. This prevents redundant and additional testing, thus improving efficiency of treatment and decreasing cost. Order Entry/Order Management: Computerised provider order entry (CPOE) for areas like electronic prescribing can improve workflow processes, prevent lost orders and eliminate ambiguities caused by illegible handwriting. Decision Support: Computerised decision-support systems have demonstrated the ability to enhance clinical performance in many aspects of health care through, for instance, drug alerts, rule-based alerts and reminders. Electronic Communication and Connectivity: Effective communication is crucial to providing highquality health care. Communication can be among health care team members, patients and other partners, such as pharmacy, laboratory and radiology. This communication and connectivity include the medical
3 record integrated within the same facility, among different facilities within the same health care system and among different systems (Thakkar & Davis, 2006). Patient Support: Many forms of patient support have shown significant effectiveness in health care in general. These forms include patient and family education and home telemonitoring. Administrative processes: Electronic scheduling systems for hospital admission, inpatient and outpatient procedures and visits play an important role not only in enhancing the efficiency of health care units, but also in providing better service to patients. Reporting and Population Health Management: This feature makes the process of reporting less labourintensive and time-consuming. It helps report patient safety and quality data and public health data. III. DATA QUALITY Fitness for use is one of the best definitions of the quality of data (Tayi & Ballou 1998), as this definition takes us beyond traditional concerns with data accuracy and with the many dimensions of data quality. Data quality includes not only data validation and verification, but also appropriateness of use (Orfanidis et al. 2004). Despite the fact that there are many frequently used dimensions such as accuracy, consistency, completeness, and timeliness, there is no consensus on a rigorously defined set of data quality dimensions (Strong et al. 1997; Tayi & Ballou 1998; Wand & Wang 1996). A. Data Qualiy Dimensions The definition of quality of data mentioned earlier states that data quality is a multi-dimensional concept. This definition implies that many other dimensions of data quality, including usefulness and usability, are important aspects of quality. Strong et al. (1997) classified these dimensions into four categories: intrinsic, accessibility, contextual and representational. Table 1 summarises some proposed data quality dimensions for information systems in general, along with their sources. Table 1: Data quality dimensions in health information systems Research Data Quality Dimensions (Ballou & Pazer, 1985) Accuracy, completeness, consistency and timeliness. (Strong et al., 1997) Accuracy, objectivity, believability, reputation, accessibility, access security, relevancy, value-added, timeliness, completeness, amount of data, interpretability, ease of understanding, concise representation, consistent representation. (Wang et al., 1995) Accessibility, interpretability, usefulness, believability. (Fox et al., 1994) Accuracy, currentness, completeness, and consistency. (Levitin & Redman, Contents (relevance, unambiguous definitions, 1995) obtainability of values), scope (comprehensiveness, essentialness), level of details (attribute granularity domain precision), composition (naturalness, occurrence identifiability, homogeneity), consistency (semantic consistency, structural consistency) and reaction to change (robustness, flexibility). B. Health-related Data Quality Dimensions many researchers have defined data quality dimensions in the context of health. The Canadian Institute for Health Information (CIHI) defined five dimensions: accuracy, timeliness, comparability, usability and relevance. Each is divided into several characteristics, and each characteristic is divided further into criteria. Table 2 shows some frameworks of health-related data quality dimensions. Most common dimensions of data quality are accuracy, completeness, consistency, correctness and timeliness. However, Batini et al. (2009) claimed that the basic set of dimensions for data quality are accuracy, completeness, consistency and timeliness. Table 2: Health-related Data Quality Dimensions Research (Canadian Institute for Health Information, 2009) (Orfanidis et al., 2004) Data Quality Dimensions Accuracy, timeliness, comparability, usability and relevance. Accessibility and availability, usability, security and confidentiality, provenance, data validation, integrity, accuracy and timeliness, completeness, and consistency. (Liaw et al., 2012) Accuracy, completeness, consistency, correctness and timeliness. C. Impact of poor data quality Enhancing the quality of care is a noticeable advantage of adopting EHR systems. Many studies (Thakkar & Davis 2006; Yoon-Flannery et al. 2008) have highlighted how such systems could enhance quality of care and support its continuity. Moreover, EHR promotes patient safety, as use of such systems improves patient safety by reducing medical errors in hospitals (Bates 2000; Bates et al. 1998). Medical errors can lead to death as, of which there are an estimated 98,000 each year in the United States, costing as much as $29 billion (Hoffman & Podgurski 2008). EHR systems could also notify patients about important changes in drug therapy (Jain et al. 2005). IV. THE PROPOSED FRAMEWORK The proposed framework was developed to tackle poorquality data that compromise the quality of care. The proposed framework has three categories of health care data quality dimensions. These categories represent the main elements of e- health systems and healthcare systems. Development of this framework went through many stages to reflect the nature of cloud-based HIS.
4 Fig. 1 The framework development process Fig. 1 shows the process of developing the proposed framework. The process began with gathering data quality dimensions in organizations and health care systems. These dimensions were filtered to eliminate redundancies. In this step, literature review and dictionaries were used to avoid having two dimensions with the same implication. The next step was to check whether the dimension was relevant to EHR function, content and requirements. After that, the resulting dimensions were grouped into three categories: information, communication and security. These are considered the main elements of e-health care systems (Shoniregun et al. 2010). Fig. 2 The flow of the output during development Fig. 2 shows the flow of reduction of the number of dimension at each stage. In the last stage, the dimensions are classified into three categories. This classification fits into our framework and covers all aspects of EHR systems, balancing comprehensiveness of dimensions with the nature of EHR systems. Fitting dimensions into the proposed framework gives a clearer definition of each dimension and helps identify what to measure and how. Fig. 3 The framework of data quality in cloud-based in health information systems The characteristics of high-quality data fit into three categories: information, communication, and security. As can be seen from Fig. 3, there are 11 data quality dimensions in a framework of three categories. The following sections discuss the categories. A. Information Information is one of the three framework categories that shape e-health care systems. Most of existing approaches have addressed information-related dimensions. This category holds all dimensions associated with data characteristics, which are: Accuracy: The extent to which registered data conforms to its actual value. Completeness: The state in which information is not missing and is sufficient for the task. Linkages between data promote the existence of further data. Consistency: Representation of data values remains the same in multiple data items in multiple locations. Relevance: The extent to which information is appropriate and useful for the intended task. Timeliness: The state in which data is up to date and its availability is on time. Usability: The ease with which data can be accessed, used, updated, understood, maintained and managed. B. Communication Communication is the second category of the framework. It concerns the correspondence between different care units. Because of this communication, EHR systems have multiple data items in multiple locations. Provenance: The source of data, shown and linked to metadata about data. Interpretability: The degree to which data can be understood.
5 C. Security Security prevents personal data from being corrupted and controls access to ensure privacy and confidentiality. V. CONCLUSION AND DISCUSSION Cloud computing is a promising platform for health information systems in order to reduce costs and improve accessibility. However, adopting such technology arises the pressing need for assessing and measuring the quality of cloudbased health information systems. This is due to the fact that data quality is a multidimensional concept, and there is no consensus on rigorously defined set of data quality dimensions. This would emphasis the need of automating the mechanism of data quality measurement and semantic interoperability (Liaw et al., 2012). Existing research focuses on data quality in generic information systems. These studies address data quality in many aspects aligned with data consumers. We developed an initial framework that concerns DQ in the context of electronic health care systems. This framework is a result of filtering the existing data quality dimensions in many research, and checking their suitability to the nature of e-health systems. The next step will be examining and evaluating the proposed framework by conducting semi-structured interviews with EHR stakeholders in order to improve this work. Candidates for our research are IT professionals, GPs and health system managers in three general hospitals. REFERENCES [1] Alliance, T. N., & Coordinator, N. (2008). Defining Key Health Information Technology Terms. Health San Francisco, 299(03), Retrieved from 133_0_0_18/10_2_hit_terms.pdf [2] Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., et al. (2010). A View of Cloud Computing. Communications of the ACM, 53(4), [3] Ballou, D. P., & Pazer, H. L. (1985). Modeling data and process quality in multi-input, multi-output information systems. Management science, 31(2), [4] Bates, D. W. (2000). Using information technology to reduce rates of medication errors in hospitals. BMJ, 320(7237), doi: /bmj [5] Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), doi: /j.future [6] Canadian Institute for Health Information. (2009). The CIHI Data Quality Framework. [7] Dillon, T., Wu, C., & Chang, E. (2010). Cloud computing: Issues and challenges th IEEE International Conference on Advanced Information Networking and Applications (pp ). Ieee. doi: /aina [8] DW, B., LL, L., DJ, C., & et al. (1998). EFfect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA: The Journal of the American Medical Association, 280(15), doi: /jama [9] Fox, C., Levitin, A., & Redman, T. (1994). THE NOTION OF DATA AND ITS quality dimensions. Information Processing & Management, 30(I), [10] Garets, D., & Davis, M. (2006). Electronic Medical Records vs. Electronic Health Records : Yes, There Is a Difference. HIMSS Analytics, Retrieved from mr vs ehr.pdf [11] Hammond, M., Hawtin, R., Gillam, L., & Oppenheim, C. (2010). Cloud computing for research. Final Report. Curtis+ Cartwright Consulting Ltd, 7. [12] Hoffman, S., & Podgurski, A. (2008). Finding a Cure: The Case for Regulation and Oversight of Electronic Health Record Systems. Harv. JL & Tech., 22, 103. [13] Häyrinen, K., Saranto, K., & Nykänen, P. (2008). Definition, structure, content, use and impacts of electronic health records: a review of the research literature. International Journal of Medical Informatics, 77(5), [14] Jain, A., Atreja, A., & Harris, C. (2005). Responding to the Rofecoxib Withdrawal Crisis : A New Model for Notifying Patients at Risk and Their Health Care Providers. Annals of internal, [15] Kim, G. R., & Lehmann, C. U. (2009). Electronic Health Records and Interoperability for Pediatric Care. In C. U. Lehmann, G. R. Kim, & K. B. Johnson (Eds.), Pediatric Informatics (pp ). Springer New York. Retrieved from [16] Kuo, A. (2011). Opportunities and Challenges of Cloud Computing to Improve Health Care Services. Journal of Medical Internet Research. Retrieved from [17] Levitin, A., & Redman, T. (1995). Quality dimensions of a conceptual view. Information Processing & Management, 31(1), doi: / (95)80008-h [18] Liaw, S. T., Rahimi, A., Ray, P., Taggart, J., Dennis, S., De Lusignan, S., Jalaludin, B., et al. (2012). Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature. International journal of medical informatics, doi: /j.ijmedinf [19] Orfanidis, L., Bamidis, P., & Eaglestone, B. (2004). Data Quality Issues in Electronic Health Records: An Adaptation Framework for the Greek Health System. Health Informatics Journal. [20] Parker, M., Stofberg, C., Harpe, R. D. la, Venter, I., & Wills, G. (2006). Data quality: How the flow of data influences data quality in a small to medium medical practice. [21] Rimal, B. P., Choi, E., & Lumb, I. (2009). A Taxonomy and Survey of Cloud Computing Systems Fifth International Joint Conference on INC, IMS and IDC, doi: /ncm [22] Shoniregun, C. A., Dube, K., & Mtenzi, F. (2010). Electronic healthcare information security. Springer. [23] Strong, D. M., Lee, Y. W., & Wang, R. Y. (1997). Data quality in context. Communications of the ACM, 40(5), [24] Tayi, G., & Ballou, D. (1998). Examining Data quality. Communications of the ACM. [25] Thakkar, M., & Davis, D. C. (2006). Risks, barriers, and benefits of EHR systems: a comparative study based on size of hospital. Perspectives in Health Information Management/AHIMA, American Health Information Management Association, 3. [26] Wager, K. A., Lee, F. W., & Glaser, J. P. (2009). Health Care Information Systems: A Practical Approach for Health Care Management (p. 5). Wiley. Retrieved from [27] Wand, Y., & Wang, R. (1996). Anchoring data quality dimensions in ontological foundations. Communications of the ACM. [28] Wang, R. Y., Reddy, M. P., & Kon, H. B. (1995). Toward quality data: An attribute-based approach. Decision Support Systems, 13(3), [29] Yoon-Flannery, K. (2008). A qualitative analysis of an electronic health record (EHR) implementation in an academic ambulatory setting. in primary care, [30] Zhang, R., & Liu, L. (2010). Security models and requirements for healthcare application clouds. Cloud Computing (CLOUD), 2010 IEEE 3rd.
TOWARD A FRAMEWORK FOR DATA QUALITY IN ELECTRONIC HEALTH RECORD
TOWARD A FRAMEWORK FOR DATA QUALITY IN ELECTRONIC HEALTH RECORD Omar Almutiry, Gary Wills and Richard Crowder School of Electronics and Computer Science, University of Southampton, Southampton, UK. {osa1a11,gbw,rmc}@ecs.soton.ac.uk
More informationAddressing the State of the Electronic Health Record (EHR)
Addressing the State of the Electronic Health Record (EHR) Agenda Definitions Attributes Differences Adoption Model Current State Challenges Implementation considerations What is it? EMR CMR EHR EPR PHR
More informationehealth, HIS, etc ehealth All information about health HMIS mhealth HIS Statistical IS Credited: Karl Brown, Rockefeller Foundation
ehealth, HIS, etc Statistical IS Health Informatics: Formal discipline that studies use of information in health All information about health HIS HMIS ehealth mhealth Tele-medicine Enterprise architecture:
More informationAnalyzing and Improving Data Quality
Analyzing and Improving Data Quality Agustina Buccella and Alejandra Cechich GIISCO Research Group Departamento de Ciencias de la Computación Universidad Nacional del Comahue Neuquen, Argentina {abuccel,acechich}@uncoma.edu.ar
More informationCloud Template, a Big Data Solution
Template, a Big Data Solution Mehdi Bahrami Electronic Engineering and Computer Science Department University of California, Merced, USA MBahrami@UCMerced.edu Abstract. Today cloud computing has become
More informationMona Osman MD, MPH, MBA
Mona Osman MD, MPH, MBA Objectives To define an Electronic Medical Record (EMR) To demonstrate the benefits of EMR To introduce the Lebanese Society of Family Medicine- EMR Reality Check The healthcare
More informationELECTRONIC RECORDS - DEFINITION
ELECTRONIC RECORDS - DEFINITION Today, in the health care domain there are many terms to describe Electronic Records: Computer-based Patient Record (CPR), Electronic Patient Record (EPR), Electronic Medical
More informationElectronic Medical Records vs. Electronic Health Records: Yes, There Is a Difference. A HIMSS Analytics TM White Paper. By Dave Garets and Mike Davis
Electronic Medical Records vs. Electronic Health Records: Yes, There Is a Difference A HIMSS Analytics TM White Paper By Dave Garets and Mike Davis Updated January 26, 2006 HIMSS Analytics, LLC 230 E.
More informationAppendix B Data Quality Dimensions
Appendix B Data Quality Dimensions Purpose Dimensions of data quality are fundamental to understanding how to improve data. This appendix summarizes, in chronological order of publication, three foundational
More informationEmergency Medical Data Management through an Enhanced Cloudbased Push Messaging Mechanism
Emergency Medical Data Management through an Enhanced Cloudbased Push Messaging Mechanism Vassiliki Koufi, Flora Malamateniou, and George Vassilacopoulos University of Piraeus, Department of Digital Systems,
More informationQuality. Data. In Context
Diane M. Strong, Yang W. Lee, and Richard Y. Wang Data A new study reveals businesses are defining Quality data quality with the consumer in mind. In Context DATA-QUALITY (DQ) PROBLEMS ARE INCREASINGLY
More informationHealth Information Technology Backgrounder
Health Information Technology Backgrounder An electronic health record (EHR) is defined by the National Alliance for Health Information Technology as an electronic record of health-related information
More informationInformation Quality for Business Intelligence. Projects
Information Quality for Business Intelligence Projects Earl Hadden Intelligent Commerce Network LLC Objectives of this presentation Understand Information Quality Problems on BI/DW Projects Define Strategic
More informationSupply Chain Platform as a Service: a Cloud Perspective on Business Collaboration
Supply Chain Platform as a Service: a Cloud Perspective on Business Collaboration Guopeng Zhao 1, 2 and Zhiqi Shen 1 1 Nanyang Technological University, Singapore 639798 2 HP Labs Singapore, Singapore
More informationEHR Definition, Scope & Context. Sam Heard for Peter Schloeffel ISO/TC 215 WG1 Aarhus, Denmark 3 Oct 2003
EHR Definition, Scope & Context Sam Heard for Peter Schloeffel ISO/TC 215 WG1 Aarhus, Denmark 3 Oct 2003 2 Agenda Background to the project A taxonomy and definitions of the EHR Scope of the EHR Context
More informationAchieving meaningful use of healthcare information technology
IBM Software Information Management Achieving meaningful use of healthcare information technology A patient registry is key to adoption of EHR 2 Achieving meaningful use of healthcare information technology
More informationReusing Meta-Base to Improve Information Quality
Reusable Conceptual Models as a Support for the Higher Information Quality 7DWMDQD :HO]HU %UXQR 6WLJOLF,YDQ 5R]PDQ 0DUMDQ 'UXåRYHF University of Maribor Maribor, Slovenia ABSTRACT Today we are faced with
More informationElectronic Health Records: Trends, Issues, Regulations & Technologies
New September 2010 Critical Report! Electronic Health Records: Trends, Issues, Regulations & Technologies A Management Primer & Update Including: EHR Definition and Role EHR vs. EMR The New Momentum in
More informationChapter 15 The Electronic Medical Record
Chapter 15 The Electronic Medical Record 8 th edition 1 Lesson 15.1 Introduction to the Electronic Medical Record Define, spell, and pronounce the terms listed in the vocabulary. Discuss the presidential
More informationCanada Health Infoway
Canada Health Infoway EHR s in the Canadian Context June 7, 2005 Mike Sheridan, COO Canada Health Infoway Healthcare Renewal In Canada National Healthcare Priorities A 10-year Plan to Strengthen Healthcare
More informationTransforming Healthcare in Emerging Markets with EMR adoption
Transforming Healthcare in Emerging Markets with EMR adoption Author Ann Geo Thekkel User Experience. Accenture, India Ann.geothekkel@accenture.com Abstract Compromising 24 countries, 35 percent of the
More information2009 Progress in Comprehensive Care for Rare Blood Disorders Conference
gordon point informatics www.nformatics.com 2009 Progress in Comprehensive Care for Rare Blood Disorders Conference Health Informatics Primer Topics 1. Background 2. Health Informatics 3. EHR, EMR, PHR...
More informationAN ANALYSIS ON CLOUD PARADIGM IN ONLINE BANKING Shreya Paul 1, Atma Prakash Singh 2 and Madhulika Sharma 3
International Journal of Advance Research In Science And Engineering http://www.ijarse.com AN ANALYSIS ON CLOUD PARADIGM IN ONLINE BANKING Shreya Paul 1, Atma Prakash Singh 2 and Madhulika Sharma 3 1 IT
More informationMedweb Telemedicine 667 Folsom Street, San Francisco, CA 94107 Phone: 415.541.9980 Fax: 415.541.9984 www.medweb.com
Medweb Telemedicine 667 Folsom Street, San Francisco, CA 94107 Phone: 415.541.9980 Fax: 415.541.9984 www.medweb.com Meaningful Use On July 16 2009, the ONC Policy Committee unanimously approved a revised
More informationHealth Information Technology in Healthcare: Frequently Asked Questions (FAQ) 1
Health Information Technology in Healthcare: Frequently Asked Questions (FAQ) 1 1. What is an Electronic Health Record (EHR), an Electronic Medical Record (EMR), a Personal Health Record (PHR) and e-prescribing?
More informationElectronic Health Record Systems and Secondary Data Use
Electronic Health Record Systems and Secondary Data Use HCQI Expert Group Meeting 10 May 2012 Jillian Oderkirk OECD/HD Background and Needs The 2010 Health Ministerial Communiqué noted that health care
More informationAn Analysis of Data Security Threats and Solutions in Cloud Computing Environment
An Analysis of Data Security Threats and Solutions in Cloud Computing Environment Rajbir Singh 1, Vivek Sharma 2 1, 2 Assistant Professor, Rayat Institute of Engineering and Information Technology Ropar,
More informationDraft Pan-Canadian Primary Health Care Electronic Medical Record Content Standard, Version 2.0 (PHC EMR CS) Frequently Asked Questions
December 2011 Draft Pan-Canadian Primary Health Care Electronic Medical Record Content Standard, Version 2.0 (PHC EMR CS) Frequently Asked Questions Background and History What is primary health care?
More informationHow To Improve Health Information Technology
The American Society For Clinical Pathology Policy Statement Health Information Technology/Informatics (Policy Number) Policy Statement: ASCP supports the implementation of standardized health information
More informationAmbulatory Electronic Mental Health Record Solution
Ambulatory Electronic Mental Health Record Solution with connection to EHR Services, delivers patient centered care model and platform for service delivery June 2014 Partners Mackenzie Health and Southlake
More informationTable of Contents. Page 1
Table of Contents Executive Summary... 2 1 CPSA Interests and Roles in ehealth... 4 1.1 CPSA Endorsement of ehealth... 4 1.2 CPSA Vision for ehealth... 5 1.3 Dependencies... 5 2 ehealth Policies and Trends...
More informationJan Duffy, Research Manager, Health Industry Insights EMEA
Healthcare Transformation: The Role of IT Healthcare Transformation: The Role of IT Jan Duffy, Research Manager, Health Industry Insights EMEA Agenda Western Europe: Healthcare IT Investment Western Europe:
More informationRisks, Barriers, and Benefits of EHR Systems: A Comparative Study Based on Size of Hospital
Risks, Barriers, and Benefits of EHR Systems: A Comparative Study Based on Size of Hospital 1 Risks, Barriers, and Benefits of EHR Systems: A Comparative Study Based on Size of Hospital by Minal Thakkar
More informationData Quality Assessment
Data Quality Assessment Leo L. Pipino, Yang W. Lee, and Richard Y. Wang How good is a company s data quality? Answering this question requires usable data quality metrics. Currently, most data quality
More informationCMS & ehr - An Update
Health Informatics in Hong Kong CMS & ehr - An Update Dr NT Cheung HA Convention 2010 CMS / epr is essential in the HA Each Day... 12,000 users 90,000 patients 8M CMS transactions 700,000 epr views In
More informationCreating a national electronic health record: The Canada Health Infoway experience
Creating a national electronic health record: The Canada Health Infoway experience Presentation by Dennis Giokas Chief Technology Officer, Canada Health Infoway October 11, 2007 Overview The need for EHR
More informationElectronic Medical Record
Altru Health System Electronic Medical Record Mark Waind, Jeff Shallman From material contributed by Marv Meier Alex Todorovic Countdown to the All-Electronic Medical Record 2 0 0 8 1 Interaction Layers
More informationUsing Health Information Technology to Improve Quality of Care: Clinical Decision Support
Using Health Information Technology to Improve Quality of Care: Clinical Decision Support Vince Fonseca, MD, MPH Director of Medical Informatics Intellica Corporation Objectives Describe the 5 health priorities
More informationHL7 Electronic Health Record System (EHR-S) Functional Model and Standard
HL7 Electronic Health Record System (EHR-S) Functional Model and Standard Ambassador Briefing Gary Dickinson Co-Chair, HL7 EHR WG gary.dickinson@ehr-standards.co 2002-2009 Health Level Seven, Inc. All
More informationThe Development of a Data Quality Framework and Strategy for. the New Zealand Ministry of Health
The Development of a Data Quality Framework and Strategy for the New Zealand Ministry of Health Karolyn Kerr Department of Information Systems and Operations Management, University of Auckland, Private
More informationElectronic Medical Records Getting It Right and Going to Scale
Electronic Medical Records Getting It Right and Going to Scale W. Ed Hammond, Ph.D. Duke University Medical Center 02/03/2000 e-hammond, Duke 0 Driving Factors Patient Safety Quality Reduction in cost
More informationComponent 6 - Health Management Information Systems. Objectives. Purpose of a Patient (medical) Record. Unit 3-1 Electronic Health Records
Component 6 - Health Management Information Systems Unit 3-1 Electronic Health Records Objectives State the similarities and differences between an EMR and an EHR Identify attributes and functions of an
More information1a-b. Title: Clinical Decision Support Helps Memorial Healthcare System Achieve 97 Percent Compliance With Pediatric Asthma Core Quality Measures
1a-b. Title: Clinical Decision Support Helps Memorial Healthcare System Achieve 97 Percent Compliance With Pediatric Asthma Core Quality Measures 2. Background Knowledge: Asthma is one of the most prevalent
More informationHealthcare Information Technology (HIT)
Healthcare Information Technology (HIT) Why State Governments Must Help Create a National Health Information Network Ian C. Bonnet Deloitte Consulting LLP October, 2005 State Leadership in developing a
More informationBenefits of Cloud Computing in EHR implementation
Benefits of Cloud Computing in EHR implementation The solution of Dedalus for application interoperability in the ehealth sector Sergio Di Bona Project Manager R&D Division DEDALUS SpA Italy sergio.dibona@dedalus.eu
More informationSpecial Topics in Vendor- Specific Systems. Outline. Results Review. Unit 4 EHR Functionality. EHR functionality. Results Review
Special Topics in Vendor- Specific Systems Unit 4 EHR Functionality EHR functionality Results Review Outline Computerized Provider Order Entry (CPOE) Documentation Billing Messaging 2 Results Review Laboratory
More informationTHE E-HEALTH JOURNEY. Ministry of Health Jamaica. Optimizing the use of ICT Applications in Health and Patient Care
THE E-HEALTH JOURNEY Ministry of Health Jamaica Optimizing the use of ICT Applications in Health and Patient Care 8 th Caribbean Conference on Health Financing Initiatives Presenter: Mr. Arnold Cooper
More informationOECD Study of Electronic Health Record Systems
OECD Study of Electronic Health Record Systems Ministry of Health of the Czech Republic E health Expert Group Meeting 19 June 2012 Jillian Oderkirk OECD/HD Background and Needs The 2010 Health Ministerial
More informationHow To Improve The Pharmaceutical Industry In Japanese
Pharmaceutical business innovation through utilization of EMR Abstract The pharmaceutical industry in Japan is in a transformation phase due to the government s tightening control over healthcare expenditure
More informationFactors Influencing an Organisation's Intention to Adopt Cloud Computing in Saudi Arabia
2014 IEEE 6th International Conference on Cloud Computing Technology and Science Factors Influencing an Organisation's Intention to Adopt Cloud Computing in Saudi Arabia Nouf Alkhater Gary Wills Robert
More informationTHE IMPACT OF CLOUD COMPUTING ON ENTERPRISE ARCHITECTURE. Johan Versendaal
THE IMPACT OF CLOUD COMPUTING ON ENTERPRISE ARCHITECTURE Johan Versendaal HU University of Applied Sciences Utrecht Nijenoord 1, 3552 AS Utrecht, Netherlands, johan.versendaal@hu.nl Utrecht University
More informationEHRs vs. Paper-based Systems: 5 Key Criteria for Ascertaining Value
Research White Paper EHRs vs. Paper-based Systems: 5 Key Criteria for Ascertaining Value Provided by: EHR, Practice Management & Billing In One www.omnimd.com Before evaluating the benefits of EHRs, one
More informationToward Meaningful Use of HIT
Toward Meaningful Use of HIT Fred D Rachman, MD Health and Medicine Policy Research Group HIE Forum March 24, 2010 Why are we talking about technology? To improve the quality of the care we provide and
More informationElectronic Prescribing
Electronic Prescribing Objectives: Describe Electronic Prescribing Discuss tools and information system needed Evaluate the Nurse Informaticist role in EMR/Electronic Prescribing Discuss safety, ethical
More informationThe Human Experiment- Electronic Medical/Health Records
The Human Experiment- Electronic Medical/Health Records Patient safety is one of the primary stated intentions behind the push for computerized medical records. To the extent illegible handwriting leads
More informationClinical Database Information System for Gbagada General Hospital
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Volume 2, Issue 9, September 2015, PP 29-37 ISSN 2349-4840 (Print) & ISSN 2349-4859 (Online) www.arcjournals.org
More informationTable of Contents. Preface... 1. 1 CPSA Position... 2. 1.1 How EMRs and Alberta Netcare are Changing Practice... 2. 2 Evolving Standards of Care...
March 2015 Table of Contents Preface... 1 1 CPSA Position... 2 1.1 How EMRs and Alberta Netcare are Changing Practice... 2 2 Evolving Standards of Care... 4 2.1 The Medical Record... 4 2.2 Shared Medical
More informationHow To Understand The Difference Between Terminology And Ontology
Terminology and Ontology in Semantic Interoperability of Electronic Health Records Dr. W. Ceusters Saarland University Semantic Interoperability Working definition: Two information systems are semantically
More informationEligible Professionals please see the document: MEDITECH Prepares You for Stage 2 of Meaningful Use: Eligible Professionals.
s Preparing for Meaningful Use in 2014 MEDITECH (Updated December 2013) Professionals please see the document: MEDITECH Prepares You for Stage 2 of Meaningful Use: Professionals. Congratulations to our
More informationMeaningful Use. Goals and Principles
Meaningful Use Goals and Principles 1 HISTORY OF MEANINGFUL USE American Recovery and Reinvestment Act, 2009 Two Programs Medicare Medicaid 3 Stages 2 ULTIMATE GOAL Enhance the quality of patient care
More informationDisclosure slide. Objectives. The Meaningful Use of EMR. The Meaningful Use of EMR. The Meaningful Use of EMR. Peter Cherouny, M.D.
Disclosure slide WAPC 2012 42 nd Annual Statewide Perinatal Conference Clinical Issues Update Green Bay, WI April 15-17, 2012 Peter Cherouny, M.D. Nothing to disclose The Meaningful Use of Electronic Health
More informationThe Impact of Cloud Computing on Saudi Organizations: The Case of a Telecom Company
International Journal of Computing Academic Research (IJCAR) ISSN 2305-9184 Volume 3, Number 6(December 2014), pp. 126-130 MEACSE Publications http://www.meacse.org/ijcar The Impact of Cloud Computing
More informationEnhancing DataQuality. Environments
Nothing is more likely to undermine the performance and business value of a data warehouse than inappropriate, misunderstood, or ignored data quality. Enhancing DataQuality in DataWarehouse Environments
More informationBI en Salud: Registro de Salud Electrónico, Estado del Arte!
BI en Salud: Registro de Salud Electrónico, Estado del Arte! Manuel Graña Romay! ENGINE Centre, Wrocław University of Technology! Grupo de Inteligencia Computacional (GIC); UPV/EHU; www.ehu.es/ ccwintco!
More informationHow To Understand The Individual Competences Of An It Manager
ORGANIZATIONS ARE GOING TO THE CLOUD: WHICH COMPETENCES FOR THE IT MANAGER? Luca Sabini, Stefano Za, Paolo Spagnoletti LUISS Guido Carli University Rome Italy {lsabini, sza, pspagnoletti}@luiss.it ABSTRACT
More informationHealthcare Services - education and research - developed in the INSEED project
Healthcare Services - education and research - developed in the INSEED project Radu DOBRESCU Universitatea Politehnica din Bucureşti Program Strategic pentru Promovarea Inovarii în Servicii prin Educaţie
More informationElectronic Medical Records. The thirty year struggle for adoption. Drew Loucks, Dwight Keysor and Lauren Peters
Electronic Medical Records The thirty year struggle for adoption Drew Loucks, Dwight Keysor and Lauren Peters Introduction Electronic medical records (EMRs) were introduced to the healthcare market in
More informationThe Big Picture: IDNT in Electronic Records Glossary
TERM DEFINITION CCI Canada Health Infoway Canadian Institute for Health Information EHR EMR EPR H L 7 (HL7) Canadian Classification of Interventions is the Canadian standard for classifying health care
More informationCommunity Health Initiatives Taking HIT to the Next Level
NEW JERSEY Chapter New York State Chapter Community Health Initiatives Taking HIT to the Next Level Elaine Remmlinger Vice President/National Service Director Kurt Salmon Associates 0 Topics Current State
More informationHIT Workflow & Redesign Specialist: Curriculum Overview
HIT Workflow & Redesign Specialist: Curriculum Overview Component - Description Units - Description Appx. Time 1: Introduction to Health Care and Public Health in the U.S. Survey of how healthcare and
More informationAuthentication Mechanism for Private Cloud of Enterprise. Abstract
Authentication Mechanism for Private Cloud of Enterprise Mei-Yu Wu *, and Shih-Pin Lo Department of Information Management, Chung Hua University, Hsinchu, Taiwan {mywu, e10010008}@chu.edu.tw Abstract Enterprises
More informationWHITEPAPER 6 EHR TRENDS to Watch in
WHITEPAPER 6 EHR TRENDS to Watch in 2015 INTRODUCTION Since the passage of the HITECH Act in 2009, the healthcare industry has undergone rapid changes in technology. The adoption of electronic health records
More informationEarly Lessons learned from strong revenue cycle performers
Healthcare Informatics June 2012 Accountable Care Organizations Early Lessons learned from strong revenue cycle performers Healthcare Informatics Accountable Care Organizations Early Lessons learned from
More informationMaster of Science in Computer Science. Option Health Information Systems
Master of Science in Computer Science Option Health Information Systems 1. The program Currently, in the Lebanese and most of Middle East s hospitals, the management of health information systems is handled
More informationClinical Decision Support
Goals and Objectives Clinical Decision Support What Is It? Where Is It? Where Is It Going? Name the different types of clinical decision support Recall the Five Rights of clinical decision support Identify
More informationAccountable Care: Implications for Managing Health Information. Quality Healthcare Through Quality Information
Accountable Care: Implications for Managing Health Information Quality Healthcare Through Quality Information Introduction Healthcare is currently experiencing a critical shift: away from the current the
More informationConcept Series Paper on Electronic Prescribing
Concept Series Paper on Electronic Prescribing E-prescribing is the use of health care technology to improve prescription accuracy, increase patient safety, and reduce costs as well as enable secure, real-time,
More informationIHE, A Taxonomy for Electronic Medical Mdi lrecords
IHE, A Taxonomy for Electronic Medical Mdi lrecords Taxonomy is defined as the science of classification; laws and principles covering the classification of objects. In our application for classifying
More informationCurrent Trends and Difficulties in Knowledge-Based e-health Systems
Current Trends and Difficulties in Knowledge-Based e-health Systems Katarzyna Ewa Pasierb, Tomasz Kajdanowicz, and Przemysław Kazienko Institute of Informatics, Wrocław University of Technology Wyb.Wyspia
More informationFrequently Asked Questions about ICD-10-CM/PCS
Frequently Asked Questions about ICD-10-CM/PCS Q: What is ICD-10-CM/PCS? A: ICD-10-CM (International Classification of Diseases -10 th Version-Clinical Modification) is designed for classifying and reporting
More informationEHR vs CCR: What is the difference between the electronic health record and the continuity of care record?
Health IT Library EHR vs CCR: What is the difference between the electronic health record and the continuity of care record? Written by C. Peter Waegemann The idea of the electronic health record was born
More informationPrivacy and Security Policies for Healthcare Solutions on the Cloud
Privacy and Security Policies for Healthcare Solutions on the Cloud Karuna P Joshi, PhD University of Maryland, Baltimore County karuna.joshi@umbc.edu Introduction Increasing adoption of technologies such
More informationDEMYSTIFYING ELECTRONIC HEALTH Presented to Central East LHIN Board of Directors. January 22, 2014
DEMYSTIFYING ELECTRONIC HEALTH Presented to Central East LHIN Board of Directors January 22, 2014 What is ehealth? What is an Electronic Health System? EHR, EMR and PHR / CIS/HIS Where does the electronic
More informationDecember 23, 2010. Dr. David Blumenthal National Coordinator for Health Information Technology Department of Health and Human Services
December 23, 2010 Dr. David Blumenthal National Coordinator for Health Information Technology Department of Health and Human Services RE: Prioritized measurement concepts Dear Dr. Blumenthal: Thank you
More information1. Introduction - Nevada E-Health Survey
1. Introduction - Nevada E-Health Survey Welcome to the Nevada E-Health Survey for health care professional providers and hospitals. The Office of Health Information Technology (OHIT) for the State of
More informationEHR Adoption and Vision for HIM
EHR Adoption and Vision for HIM Christina M. Janus, MBA, RHIA EOHIMA Spring Seminar April 14, 2007 1 Content Covered Key EHR Functions Adoption Model Group Share of Current Technologies & Vision for the
More informationMeaningful Use. Medicare and Medicaid EHR Incentive Programs
Meaningful Use Medicare and Medicaid Table of Contents What is Meaningful Use?... 1 Table 1: Patient Benefits... 2 What is an EP?... 4 How are Registration and Attestation Being Handled?... 5 What are
More informationMeaningful Use Stage 2 Certification: A Guide for EHR Product Managers
Meaningful Use Stage 2 Certification: A Guide for EHR Product Managers Terminology Management is a foundational element to satisfying the Meaningful Use Stage 2 criteria and due to its complexity, and
More informationA Medical Decision Support System (DSS) for Ubiquitous Healthcare Diagnosis System
, pp. 237-244 http://dx.doi.org/10.14257/ijseia.2014.8.10.22 A Medical Decision Support System (DSS) for Ubiquitous Healthcare Diagnosis System Regin Joy Conejar 1 and Haeng-Kon Kim 1* 1 School of Information
More informationTopic: Overview of BMI. Baldi/Hayes/Smyth: Introduction to Biomedical Informatics: 1
Introduction to Biomedical Informatics Topic: Overview of BMI Funded by NIH Grant XYZ Baldi/Hayes/Smyth: Introduction to Biomedical Informatics: 1 Outline Introduction to the class Motivating example:
More informationPost-Implementation EMR Evaluation for the Beta Ambulatory Care Clinic Proposed Plan Jul 6/2012, Version 2.0
1. Purpose and Scope Post-Implementation EMR Evaluation for the Beta Ambulatory Care Clinic Proposed Plan Jul 6/2012, Version 2.0 This document describes our proposed plan to conduct a formative evaluation
More informationCentricity Enterprise Provider Tools
GE Healthcare Centricity Enterprise Provider Tools The clinical information system that gives you critical data when and where you need it. As a healthcare provider, your patients rely on you to deliver
More informationReport on the Dagstuhl Seminar Data Quality on the Web
Report on the Dagstuhl Seminar Data Quality on the Web Michael Gertz M. Tamer Özsu Gunter Saake Kai-Uwe Sattler U of California at Davis, U.S.A. U of Waterloo, Canada U of Magdeburg, Germany TU Ilmenau,
More informationHEALTH INFORMATION TECHNOLOGY*
GLOSSARY of COMMON TERMS and ACRONYMS In HEALTH INFORMATION TECHNOLOGY* (April 2011) AHIC American Health Information Community The AHIC was a federal advisory panel created by HHS to make recommendations
More informationBehavioral Health MITA. Maturity Model Document Version 2.0. Developed for Centers for Medicare & Medicaid Services
CENTERS for MEDICARE & MEDICAID SERVICES Behavioral Health MITA Maturity Model Document Version 2.0 Developed for Centers for Medicare & Medicaid Services Behavioral Health MITA Maturity Model Document
More informationAn Overview on Important Aspects of Cloud Computing
An Overview on Important Aspects of Cloud Computing 1 Masthan Patnaik, 2 Ruksana Begum 1 Asst. Professor, 2 Final M Tech Student 1,2 Dept of Computer Science and Engineering 1,2 Laxminarayan Institute
More informationOPTIMIZING THE USE OF YOUR ELECTRONIC HEALTH RECORD. A collaborative training offered by Highmark and the Pittsburgh Regional Health Initiative
OPTIMIZING THE USE OF YOUR ELECTRONIC HEALTH RECORD A collaborative training offered by Highmark and the Pittsburgh Regional Health Initiative Introductions Disclosures Successful completion of training
More informationHealthcare Professional. Driving to the Future 11 March 7, 2011
Clinical Analytics for the Practicing Healthcare Professional Driving to the Future 11 March 7, 2011 Michael O. Bice Agenda Clinical informatics as context for clinical analytics Uniqueness of medical
More informationI n t e r S y S t e m S W h I t e P a P e r F O R H E A L T H C A R E IT E X E C U T I V E S. In accountable care
I n t e r S y S t e m S W h I t e P a P e r F O R H E A L T H C A R E IT E X E C U T I V E S The Role of healthcare InfoRmaTIcs In accountable care I n t e r S y S t e m S W h I t e P a P e r F OR H E
More informationModeling Temporal Data in Electronic Health Record Systems
International Journal of Information Science and Intelligent System, 3(3): 51-60, 2014 Modeling Temporal Data in Electronic Health Record Systems Chafiqa Radjai 1, Idir Rassoul², Vytautas Čyras 3 1,2 Mouloud
More information